Can Chemotherapy-Related Acute Care Visits Be Accurately Identified in Administrative Data?

Purpose: There is increasing interest in using administrative data to examine treatment-related complications that lead to emergency department (ED) visits or hospitalizations (H). The purpose of this study was to evaluate the reliability of billing codes for identifying chemotherapy-related acute care visits (CRVs) among women with early-stage breast cancer. Materials and Methods: The cohort was identified by using deterministically linked health databases and consisted of women who were diagnosed with early-stage breast cancer who started adjuvant chemotherapy between 2007 and 2009 in Ontario, Canada. A random sample of 496 patient cases was chosen as the validation cohort. Sensitivity (SN) and specificity (SP) were calculated for three scenarios: chemotherapy-related ED visit, chemotherapy-related H, and febrile neutropenia (FN)–related visit. For FN-related visits, three definitions were considered: general, moderate, and strict. Results: The administrative cohort consisted of 8,359 patients, 43.4% of whom had at least one ED or H, including 1,496 women who had multiple visits that resulted in 6,293 unique visits. Of these, 73.1% were considered CRVs. The algorithm performed well in identifying CRVs that included H either from ED (SN, 90%; SP, 100%) or directly from home (SN, 91%; SP, 93%), but less well for ED visits that did not result in H (SN, 65%; SP, 80%). Depending on which FN algorithm was used, 4.8% to 24% of visits were considered related. The moderate FN algorithm provided the best tradeoff between SN (69% to 97%) and SP (83% to 98%). Conclusion: Administrative data can be valuable in evaluating chemotherapy-related serious events. Algorithm validation in other cohorts is needed.

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Impact of treatment regimen on acute care use during and after adjuvant chemotherapy for early-stage breast cancer

Breast Cancer Research and Treatment ◽

10.1007/s10549-017-4280-y ◽

2017 ◽

Vol 164 (3) ◽

pp. 515-525 ◽

Cited By ~ 9

Author(s):

Kathryn J. Ruddy ◽

Holly K. Van Houten ◽

Lindsey R. Sangaralingham ◽

Rachel A. Freedman ◽

Carrie A. Thompson ◽

...

Keyword(s):

Breast Cancer ◽

Adjuvant Chemotherapy ◽

Treatment Regimen ◽

Acute Care ◽

Early Stage ◽

Early Stage Breast Cancer

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Can we accurately identify chemotherapy-related acute care visits in administrative data?

Journal of Clinical Oncology ◽

10.1200/jco.2014.32.30_suppl.185 ◽

2014 ◽

Vol 32 (30_suppl) ◽

pp. 185-185 ◽

Cited By ~ 1

Author(s):

Monika K. Krzyzanowska ◽

Katherine Enright ◽

Rahim Moineddin ◽

Lingsong Yun ◽

Mohammed Ghannam ◽

...

Keyword(s):

Breast Cancer ◽

Adjuvant Chemotherapy ◽

Acute Care ◽

Administrative Data ◽

Population Based ◽

Emergency Department Visits ◽

Diagnosis Code ◽

Diagnosis Codes ◽

Chart Abstraction ◽

Ed Visits

185 Background: Administrative data is increasingly being used to study treatment related complications that lead to acute care visits such as emergency department visits or hospitalizations (ED+H). We evaluated the accuracy of diagnosis codes for identifying chemotherapy related acute care visits (CRVs) among women with breast cancer. Methods: We prospectively developed algorithms to identify CRVs from administrative data in women receiving adjuvant chemotherapy for breast cancer in Ontario, Canada. Sensitivity (SN) and specificity (SP) were calculated for 3 scenarios: chemotherapy related ED visit, chemotherapy related H, and febrile neutropenia (FN) related visit using the chart as the gold standard. Since there is no specific diagnosis code for FN, three definitions of FN were considered: general (defined as fever or infection or neutropenia as main reason for visit), moderate (neutropenia as main reason for visit) or strict (fever or infection plus neutropenia). The population based cohort was generated by linking several health databases to identify women who had at least one ED+H during adjuvant chemotherapy for breast cancer between 2007-2009. The validation cohort consisted of 490 randomly selected cases from this cohort. Results: The population-based cohort consisted of 8,359 patients of whom 43.4% had at least one ED+H including 1,496 women who had multiple visits resulting in 6,293 unique ED+H. Of these, 73.1% were considered CRVs based on our algorithm. The algorithm performed well in identifying CRVs that included an H either from ED (SN 90%, SP 100%) or directly from home (SN 91%, SP 93%) but less well for ED visits that did not result in H (SN 65%, SP 80%). Depending on which FN algorithm was used, 1.4-24% of visits were considered FN related. The general FN algorithm had excellent SN regardless of whether the visit involved H (94-98%) but SP was moderate (66-80%). The strict FN algorithm had good SP (78-99%) but SN was highly variable (13-89%). The moderate FN algorithm provided the best tradeoff between SN (69-97%) and SP (76-98%). Conclusions: CRVs can be identified from administrative data with reasonable confidence, obviating the need for chart abstraction to evaluate chemotherapy related serious events.

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